Informs Annual Meeting 2017

SB03A

INFORMS Houston – 2017

Sunday, 11:00AM - 12:30PM

4 - Panelist Eric Bickel, University of Texas, Austin, TX, United States, ebickel@mail.utexas.edu 5 - Panelist James R. Driscoll, Intel Corp., Beaverton, OR, United States, james.r.driscoll@intel.com

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310A Game Theoretical Models in Strategic Managament Sponsored: Decision Analysis Sponsored Session Chair: Wenxin Xu, University of Illinois at Urbana-Champaign, 1206 S Sixth Street, (217) 503-8779, IL, 61820, United States, wenxin.v.xu@polyu.edu.hk 1 - Uncertain Innovation, Spillovers, and First and Second Mover Advantages Jovan Grahovac, Purdue University, 403 W State Street, West Lafayette, IN, 47907, United States, jgrahov@purdue.edu, Dharma Kwon, Wenxin Xu We use a game theoretic duopoly model to examine the impact of spillovers and uncertain R&D expenditures and completion times on firm outcomes. We characterize the Nash equilibrium of the model and we find that spillovers may or may not diminish the profit of the firm that is more capable in R&D. In other words, the technological leader may wish to commit to imitability of its innovation under certain conditions. Moreover, we find that the technological leader does not always invest earlier than the laggard, and we discuss implications of these results for firm strategy and the resource-based view. 2 - Exit Strategy under Uncertainty and Incomplete Information Dharma Kwon, University of Illinois at U-C, 350 Wohlers Hall, 1206 S Sixth Street Mc-706, Champaign, IL, 61820, United States, dhkwon@illinois.edu We examine a duopoly exit game between two firms where the the flow profits are stochastic and the exit cost of each firm is private information, and we characterize the Markov perfect Bayesian equilibria. 3 - Innovation Effort, Spillovers and Patent Licensing Strategy Benoit Chevalier-Roignant, King’s College London, London, United Kingdom, benoit.chevalier-roignant@kcl.ac.uk In this paper, we endogenize the choice between licensing a technology or keep it proprietary. An innovator decides on the innovation effort to achieve a process innovation, on whether (and under which conditions) to license the technology and, when demand is known, on supply. We examine the growth and volatility conditions which drive the choice of licensing contracts and the level of innovation effort. 4 - R & D Competition with Spillovers and Uncertain Completion Times Wenxin Xu, The Hong Kong Polytechnic University, wenxu@polyu.edu.hk We examine a game-theoretic model of two firms that are competitively engaged in research and development (R & D) projects. We investigate the impact of imitability on R&D investment strategies when the R & D completion times are uncertain and either firm can receive spillover from the other. 310B Panel: The Journey to Organizational Decision Quality Sponsored: Decision Analysis Sponsored Session Chair: Carl S. Spetzler, Strategic Decisions Group, Palo Alto, CA, 94301-2411, United States, cspetzler@sdg.com 1 - Moderator Carl S. Spetzler, Strategic Decisions Group, 745 Emerson Street, Palo Alto, CA, 94301-2411, United States, cspetzler@sdg.com In this session we will review what it means to reach ODQ (organizational decision quality) using the assessment instrument that has been generated by the board of examiners for the Raiffa-Howard Award. The panel members will then discuss the challenges they faced and currently face on the journey to ODQ and share lessons that have been learned along the way. 2 - Panelist Bill K. Klimack, Chevron, BillKlimack@chevron.com 3 - Panelist Dinesh Cheryan, Hess Corporation, Houston, TX, United States, dinesh.cheryan@Hess.com SB02

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310C Operations Research Approaches for Building Demand Response Invited: Tutorial Invited Session Chair: Jiming Peng, University of Houston, Houston, TX, !, United States, jopeng@Central.uh.edu Co-Chair: Rajan Batta, University at Buffalo (SUNY), 410 Bell Hall, University at Buffalo (SUNY), Buffalo, NY, 14260, United States, batta@buffalo.edu 1 - Operations Research Approaches for Building Demand Response in a Smart Grid Electric power systems need to ensure that production and demand of electricity are continuously in balance. With fundamental changes taking place in the power grids of many countries due to a variety of technological and policy developments, there is a need to obtain additional flexibility to achieve this essential power balance. Demand response refers to the collection of all the means to obtain this flexibility from the demand side of the balance. We present a selection of contributions of operations research to the provision of demand response by the residential, commercial and institutional sectors of the economy. The aspects covered include electricity tariffs, building energy management systems, load estimation, local generation, electric vehicles, energy storage, and building-level aggregation. We conclude with a brief discussion of current opportunities for operations research to support the development and realization of the potential of demand response. SB03A Grand Ballroom A Joint Session RMP/APS: Topics in Revenue Management and Applied Probability Sponsored: Applied Probability Sponsored Session Chair: N. Bora Keskin, Duke University, Durham, NC, 27708-0120, United States, bora.keskin@duke.edu 1 - Revenue Management in Systems of Reusable Resources David Simchi-Levi, Massachusetts Institute of Technology, Dept of Civil and Environmental Engineering, 77 Massachusetts Avenue Rm 1-171, Cambridge, MA, 02139, United States, dslevi@mit.edu We first consider personalized assortment optimization in systems of reusable resources. When the manager is not privy to real-time utilization data we show that the optimal policy can be computed efficiently. When real-time utilization data is available, we propose effective heuristic policies. We further consider the joint pricing and assortment problem and show that non-discriminatory policies can be developed in special cases despite theoretical hardness. Motivated by practical considerations we extend these techniques to settings in which demand varies periodically over time. 2 - Less Can be More in Price Experimentation Divya Singhvi, Massachusetts Institute of Technology, Cambridge, MA, United States, dsinghv@mit.edu, Georgia Perakis We consider a dynamic pricing problem where a monopolist is selling a single product but has no knowledge of the demand curve. Further, there is a cost on price experimentation. The monopolist seeks to efficiently learn the demand curve and keep the cost of price experimentation low. We propose an approach for price experimenting and learning which is simple and mimics industry practice. We provide bounds on the number of price experimentations needed to achieve a threshold revenue level. We show that with few price experimentations (aka 4) we can be within 18% of the optimal. Miguel Anjos, Polytechnique Montreal, Montreal, QC, United States, miguel-f.anjos@polymtl.ca, Juan Gómez

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